Autoencoder-Based Unequal Error Protection Codes
Journal article, 2021

We present a novel autoencoder-based approach for designing codes that provide unequal error protection (UEP) capabilities. The proposed approach, based on a generalization of an autoencoder loss function, provides a versatile framework for the design of message-wise and bit-wise UEP codes. Using an associated weight vector, the generalized loss function can be used to trade off error probabilities corresponding to different importance classes and to explore the region of achievable error probabilities. For message-wise UEP, we compare the proposed autoencoder-based UEP codes with a union of random coset codes. For bit-wise UEP, the proposed codes are compared with UEP rateless spinal codes and the superposition of random Gaussian codes. In all cases, the autoencoder-based codes show superior performance while providing design simplicity and flexibility in trading off error protection among different importance classes.

unequal error protection

Decoding

Error correction codes

Training

Receivers

Neural networks

Error probability

deep learning

Transmitters

Autoencoders

Author

Vukan Ninkovic

University of Novi Sad

Dejan Vukobratovic

University of Novi Sad

Christian Häger

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

Alexandre Graell I Amat

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

IEEE Communications Letters

1089-7798 (ISSN)

Vol. 25 11 3575-3579

INnovation and excellence in massive-scale COMmunications and information processING - INCOMING

European Commission (EC) (EC/H2020/856967), 2020-01-01 -- 2022-12-31.

Areas of Advance

Information and Communication Technology

Subject Categories

Telecommunications

Embedded Systems

Probability Theory and Statistics

DOI

10.1109/LCOMM.2021.3108845

More information

Latest update

2/25/2022